I will describe the votlage, megawatts, megavars (receieved and delivered), and power factor (lagging and leading) in terms of the electricity. Key goals are to understand substations and meters with regards to voltage and power factor as well as megawatts and megavars.
Electric utilities collect meter readings in time intervals in various units. The intervals can be from 1 minute to 60 minute intervals collecing kW, kWh, VARh, volts. An utility has to maintain a specified range for voltage across their electric network. When voltage is too low brown outs or electric motors may fail to work and when voltage is too high appliances and equiment can overheat, burn up, and possibly explode.
A negative power factor (Lagging) occurs when the device (which is normally the load) generates power, which then flows back towards the source, which is normally considered the generator.
In an electric power system, a load with a low power factor draws more current than a load with a high power factor for the same amount of useful power transferred.
The higher currents increase the energy lost in the distribution system, and require larger wires and other equipment. Because of the costs of larger equipment and wasted energy, electrical utilities will usually charge a higher cost to industrial or commercial customers where there is a low power factor.
Power factors below 1.0 require a utility to generate more than the minimum volt-amperes necessary to supply the real power (watts).
This increases generation and transmission costs. For example, if the load power factor were as low as 0.7, the apparent power would be 1.4 times the real power used by the load. Line current in the circuit would also be 1.4 times the current required at 1.0 power factor, so the losses in the circuit would be doubled (since they are proportional to the square of the current).
Alternatively all components of the system such as generators, conductors, transformers, and switchgear would be increased in size (and cost) to carry the extra current.
Utilities typically charge additional costs to commercial customers who have a power factor below some limit, which is typically 0.9 to 0.95. Engineers are often interested in the power factor of a load as one of the factors that affect the efficiency of power transmission.
Since the units are consistent, the power factor is by definition a dimensionless number between −1 and 1. When power factor is equal to 0, the energy flow is entirely reactive and stored energy in the load returns to the source on each cycle. When the power factor is 1, all the energy supplied by the source is consumed by the load.
Power factors are usually stated as “leading” or “lagging” to show the sign of the phase angle. Capacitive loads are leading (current leads voltage) and supply power, and inductive loads are lagging (current lags voltage) and consume power.
## 'data.frame': 3033369 obs. of 4 variables:
## $ readdate : Factor w/ 1056 levels "2016-02-28 00:15:00",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Voltage : num 235 237 237 236 236 ...
## $ substationName: Factor w/ 24 levels "Boynton Valley",..: 23 23 23 23 23 23 23 23 23 23 ...
## $ meter : int 300063 300063 300063 300063 300063 300063 300063 300063 300063 300063 ...
## 'data.frame': 10800 obs. of 4 variables:
## $ ReadValue: num 0 0 0 0 0 0 0 0 0 0 ...
## $ station : Factor w/ 15 levels "BOYN","CHPH",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ name : Factor w/ 3 levels "PMQD3D","PMQD3R",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ReadDate : Factor w/ 240 levels "2016-02-28 00:10:00.000",..: 1 2 3 4 5 6 235 236 237 238 ...
Range of days: 2016-02-28, 2016-03-09
Summary of Voltage: 100.6, 120.8, 122.4, 122, 123.4, 130
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 100.6 120.8 122.4 122.0 123.4 130.0
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 5.832 7.704 8.962 10.840 25.240
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -3.572 -1.274 -0.754 -0.237 0.752 7.080 240
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.567 1.210 3.040 2.760 50.130 240
The power factor dataset contains 10,800 obs. of 4 variables which are readings taken every 60 minutes.
* ReadValue: num
* station : 4 Charcter Desc of substation in this system (SCADA System)
* name : Unit of Measure
* mega watt
* mvars delievered
* mvars received
* ReadDate : string in yyyy-mm-dd hh:mm:ss format
The power factor dataset contains 3,033,369 obs. of 4 variables which are readings taken every 15 minutes. * readdate: string in yyyy-mm-dd hh:mm:ss format
* Voltage : integer
* substationName: string of full substation name
* meter : integer
The readings for each meter occur every 15 minutes.
## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame': 3031348 obs. of 10 variables:
## $ substationName: chr "CORNERSVILLE" "BOYNTON VALLEY" "CORNERSVILLE" "WARTRACE" ...
## $ meter : int 200754 300615 200045 202095 301042 301144 300434 200372 301639 200728 ...
## $ readdate : Factor w/ 1056 levels "2016-02-28 00:15:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ dtReadDate : POSIXct, format: "2016-02-28" "2016-02-28" ...
## $ dtReadDay : POSIXct, format: "2016-02-28" "2016-02-28" ...
## $ h : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hm : chr "00:15" "00:15" "00:15" "00:15" ...
## $ Voltage : num 226 230 230 230 230 ...
## $ VoltsHalf : num 113 115 115 115 115 ...
## $ voltage.bucket: Factor w/ 4 levels "(0,114]","(114,120]",..: 1 2 2 2 2 2 2 2 2 2 ...
## - attr(*, "vars")=List of 4
## ..$ : symbol readdate
## ..$ : symbol Voltage
## ..$ : symbol substationName
## ..$ : symbol meter
## - attr(*, "drop")= logi TRUE
## - attr(*, "indices")=List of 3031348
## ..$ : int 0
## ..$ : int 1
## ..$ : int 2
## ..$ : int 3
## ..$ : int 4
## ..$ : int 5
## ..$ : int 6
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## ..$ : int 49
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## ..$ : int 83
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## ..$ : int 96
## ..$ : int 97
## ..$ : int 98
## .. [list output truncated]
## - attr(*, "group_sizes")= int 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "biggest_group_size")= int 1
## - attr(*, "labels")='data.frame': 3031348 obs. of 4 variables:
## ..$ readdate : Factor w/ 1056 levels "2016-02-28 00:15:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ Voltage : num 226 230 230 230 230 ...
## ..$ substationName: chr "CORNERSVILLE" "BOYNTON VALLEY" "CORNERSVILLE" "WARTRACE" ...
## ..$ meter : int 200754 300615 200045 202095 301042 301144 300434 200372 301639 200728 ...
## ..- attr(*, "vars")=List of 4
## .. ..$ : symbol readdate
## .. ..$ : symbol Voltage
## .. ..$ : symbol substationName
## .. ..$ : symbol meter
## ..- attr(*, "drop")= logi TRUE
## ..- attr(*, "indices")=List of 3031348
## .. ..$ : int 110886
## .. ..$ : int 80986
## .. ..$ : int 1153179
## .. ..$ : int 107440
## .. ..$ : int 49808
## .. ..$ : int 64532
## .. ..$ : int 47585
## .. ..$ : int 1869068
## .. ..$ : int 128406
## .. ..$ : int 45412
## .. ..$ : int 74532
## .. ..$ : int 36812
## .. ..$ : int 1891005
## .. ..$ : int 39526
## .. ..$ : int 104268
## .. ..$ : int 53385
## .. ..$ : int 87200
## .. ..$ : int 72824
## .. ..$ : int 52936
## .. ..$ : int 24564
## .. ..$ : int 61724
## .. ..$ : int 130552
## .. ..$ : int 127927
## .. ..$ : int 61398
## .. ..$ : int 93300
## .. ..$ : int 26786
## .. ..$ : int 49706
## .. ..$ : int 721
## .. ..$ : int 16091
## .. ..$ : int 107630
## .. ..$ : int 1851353
## .. ..$ : int 33630
## .. ..$ : int 1323
## .. ..$ : int 13715
## .. ..$ : int 2448465
## .. ..$ : int 147372
## .. ..$ : int 98012
## .. ..$ : int 159207
## .. ..$ : int 131493
## .. ..$ : int 2864736
## .. ..$ : int 2455424
## .. ..$ : int 11477
## .. ..$ : int 1326643
## .. ..$ : int 35332
## .. ..$ : int 47917
## .. ..$ : int 40619
## .. ..$ : int 63706
## .. ..$ : int 0
## .. ..$ : int 1097803
## .. ..$ : int 125292
## .. ..$ : int 101883
## .. ..$ : int 81390
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## .. ..$ : int 1868829
## .. ..$ : int 2339343
## .. ..$ : int 2867704
## .. ..$ : int 1883569
## .. ..$ : int 1797321
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## .. ..$ : int 126585
## .. ..$ : int 3243
## .. ..$ : int 112652
## .. ..$ : int 125924
## .. ..$ : int 1220821
## .. ..$ : int 2849519
## .. ..$ : int 2851543
## .. ..$ : int 73971
## .. ..$ : int 81082
## .. ..$ : int 131272
## .. ..$ : int 10127
## .. ..$ : int 40353
## .. ..$ : int 2317947
## .. ..$ : int 90717
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## .. ..$ : int 6107
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## .. ..$ : int 1843042
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## .. ..$ : int 50494
## .. ..$ : int 1851837
## .. ..$ : int 19241
## .. ..$ : int 1972168
## .. ..$ : int 1964485
## .. ..$ : int 1198276
## .. ..$ : int 89813
## .. ..$ : int 84640
## .. ..$ : int 1957184
## .. ..$ : int 81591
## .. ..$ : int 873231
## .. ..$ : int 1965374
## .. ..$ : int 101051
## .. ..$ : int 625
## .. ..$ : int 105202
## .. ..$ : int 1337423
## .. ..$ : int 1462896
## .. ..$ : int 104237
## .. .. [list output truncated]
## ..- attr(*, "group_sizes")= int 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "biggest_group_size")= int 1
## Classes 'tbl_df', 'tbl' and 'data.frame': 3600 obs. of 15 variables:
## $ substationName: chr "BOYNTON VALLEY" "BOYNTON VALLEY" "BOYNTON VALLEY" "BOYNTON VALLEY" ...
## $ ReadDate : Factor w/ 240 levels "2016-02-28 00:10:00.000",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ dtReadDate : POSIXct, format: "2016-02-28 00:00:00" "2016-02-28 01:00:00" ...
## $ dtReadDay : POSIXct, format: "2016-02-28" "2016-02-28" ...
## $ h : num 0 1 2 3 4 5 6 7 8 9 ...
## $ hm : chr "00:15" "01:15" "02:15" "03:15" ...
## $ mw : num 9.33 9.34 9.36 9.55 9.81 ...
## $ mvar.delivered: num 0 0 0 0 0 0 0 0 0 0 ...
## $ mvar.received : num 0.84 0.771 0.742 0.712 0.675 ...
## $ mvar : num -0.84 -0.771 -0.742 -0.712 -0.675 ...
## $ mwsquared : num 87.1 87.2 87.7 91.3 96.3 ...
## $ mvarsquared : num 0.706 0.594 0.551 0.507 0.456 ...
## $ pf : num 0.996 0.997 0.997 0.997 0.998 ...
## $ pfChart : num 1 1 1 1 1 ...
## $ desc : Factor w/ 2 levels "Lagging","Leading": 2 2 2 2 2 2 2 2 2 2 ...
## 'data.frame': 27 obs. of 3 variables:
## $ station : Factor w/ 27 levels "BOYN","CHPH",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ substationName: Factor w/ 27 levels "BOYNTON VALLEY",..: 1 2 3 4 5 6 25 7 8 9 ...
## $ prettyName : Factor w/ 27 levels "Boynton Valley",..: 1 2 3 4 5 6 25 7 8 9 ...
Voltage and Power Factor by time interval for each deliver point (substation) are the main features. One goal is to determine if we can predict power factor.
investigation into your feature(s) of interest? * mega watt
* mega var delivered
* mega var received
I created the power factor, mvars and the direction of the power factor (lagging and leading).
New variables where created for these datasets:
Williamsport and Mt Pleasent have bimodel distibutions of voltage.
The long leading tail on the voltage histogram, has a larger range in the data in the lower range than in the upper range.
Number of voltage intervals < 117: 43458
The range is : 100.6, 116.95
Number of voltage intervals > 126: 41439
The range is : 126.05, 129.95
Various methods were used to clean the data. For instance the ReadDates for the voltage intervals are in ending interval. The interval starts at 3/2/2016 00:15min and ends on 3/3/2016 00:00. To associate the 15 minute intervals with the correct hour and day, we had to roll back each 15 minute interval by 15 minutes.
The power factor data needed to be pivoted to get the data into a tidy format as well. The orignal data has the MegaWatt, MegaVars Delivered and Received in the same column, these values were split out into their own columns.
The substation names can have leading and trailing spaces so this data needed to be trimed.
The scatter plot shows the data along the time axis for the intervals for the day. The interesting point in this chart, which is similar to the histogram is how the shading changes from dark to grey, which is the points stacking on top of each other.
It takes 5 points on top of each other to make a solid point on this chart.
This demonstrates how the data is spread out over the ranges through the day by hour.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 100.6 120.8 122.4 122.0 123.4 130.0
## [1] 2.047819
## [1] 100.60 129.95
## 0% 25% 50% 75% 100%
## 100.60 120.80 122.35 123.45 129.95
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 5.832 7.704 8.962 10.840 25.240
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -3.572 -1.274 -0.754 -0.237 0.752 7.080 240
Meters where more than 4 intervals below the 114 volts threshold.
| substationName | meter | dtReadDay | count |
|---|---|---|---|
| CORNERSVILLE | 301146 | 2016-03-03 | 35 |
| BOYNTON VALLEY | 200227 | 2016-03-03 | 31 |
| CORNERSVILLE | 301146 | 2016-02-28 | 28 |
| CORNERSVILLE | 300482 | 2016-03-03 | 27 |
| UNIONVILLE | 300964 | 2016-03-03 | 27 |
| CORNERSVILLE | 301146 | 2016-03-01 | 23 |
| CORNERSVILLE | 301146 | 2016-03-07 | 23 |
| CORNERSVILLE | 300482 | 2016-03-01 | 21 |
| FOUNDRY HILL | 301042 | 2016-03-05 | 21 |
| CORNERSVILLE | 301089 | 2016-02-28 | 20 |
Meters where more than 4 intervals below the 126 volts threshold.
| substationName | meter | dtReadDay | count |
|---|---|---|---|
| BOYNTON VALLEY | 200037 | 2016-03-02 | 1 |
| BOYNTON VALLEY | 200037 | 2016-03-08 | 1 |
| BOYNTON VALLEY | 200037 | 2016-03-09 | 1 |
| BOYNTON VALLEY | 200037 | 2016-03-06 | 3 |
| BOYNTON VALLEY | 200037 | 2016-02-29 | 7 |
| BOYNTON VALLEY | 200038 | 2016-03-02 | 1 |
| BOYNTON VALLEY | 200038 | 2016-03-08 | 1 |
| BOYNTON VALLEY | 200038 | 2016-03-09 | 1 |
| BOYNTON VALLEY | 200038 | 2016-03-06 | 5 |
| BOYNTON VALLEY | 200038 | 2016-02-29 | 10 |
By Hour
By Hour
This shows an interesting trend starting at 11AM (11:00 hours) until 10PM (20:00 hrs). The power factor spreads over a wider range. This is interesting on a system wide review, however we are more concerned with the power factor for each delivery point.
The strongest relationship I found is between megawatts and power factor.
As the megawatts increases the power factor approaches the 1, for each of the Substations for this single day investigation.
Coefficient, r
| Strength of Association | Positive | Negative |
|---|---|---|
| Small | .1 to .3 | -0.1 to -0.3 |
| Medium | .3 to .5 | -0.3 to -0.5 |
| Large | .5 to 1.0 | -0.5 to -1.0 |
Review the correlation between power factor and voltage using Pearsons.
Review the correlation between power factor and voltage using Pearsons.
##
## Pearson's product-moment correlation
##
## data: summary_total$v.mean and summary_total$pf
## t = -3.7994, df = 3104, p-value = 0.0001478
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10296188 -0.03294618
## sample estimates:
## cor
## -0.0680378
Counts of the voltage instances in each bucketed range.
## (0,114] (114,120] (120,126] (126,140]
## 1958 520655 2467296 41439
| substationName | (0,114] | (114,120] | (120,126] | (126,140] |
|---|---|---|---|---|
| BOYNTON VALLEY | 301 | 88157 | 124288 | 757 |
| CCJIP | NA | 1902 | 51569 | NA |
| CHAPEL HILL | 33 | 10144 | 174030 | 910 |
| COLUMBIA PRIMARY | NA | 2126 | 22387 | 772 |
| CORNERSVILLE | 729 | 119109 | 165467 | NA |
| COWAN | NA | 68 | 17912 | 29224 |
| CULLEOKA | 10 | 11834 | 74330 | NA |
| DECHERD | 41 | 5333 | 189046 | 283 |
| EAST SHELBYVILLE | 153 | 12513 | 102192 | NA |
| ESTILL SPRINGS | 19 | 6722 | 108056 | 30 |
| FOUNDRY HILL | 114 | 48420 | 395732 | 94 |
| HILLSBORO | 55 | 19766 | 157340 | 22 |
| KS PHILLIPS | NA | 1472 | 81805 | 41 |
| LYNCHBURG | NA | 2243 | 92117 | 1089 |
| MANCHESTER 161 | 92 | 18374 | 204109 | 692 |
| MT PLEASANT | 2 | 1659 | 13115 | 1961 |
| RALLY HILL | 49 | 11799 | 88399 | NA |
| RED HILL | NA | 967 | 40210 | NA |
| SALEM | 2 | 1875 | 28064 | 25 |
| SEWANEE | 10 | 7568 | 19394 | NA |
| SPRING HILL | 1 | 475 | 88591 | 2 |
| UNIONVILLE | 185 | 62565 | 85819 | NA |
| WARTRACE | 155 | 82129 | 113007 | 6 |
| WILLIAMSPORT | 7 | 3435 | 30317 | 5531 |
This chart demonstrates how the voltage can vary on the lower and upper ends of the voltage ranges.
investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
The voltage and power factor for the entire system seem to track or follow a similar trend here. They have similar shapes. Or at least when the power factor range increases the voltage drops less in the system. This could be for many reasons.
Predict Power Factor… is the goal ### OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.
Are the final three plots varied and do they meet some of the following criteria:
1 Draw comparisons.
2 Identify trends.
3 Engage a wide audience.
4 Explain a complicated finding.
5 Clarify a gap between perception and reality.
6 Enable the reader to digest large amounts of information.
Each plot reveals an important and different comparison or trend in the data. The plots incorporate many of the variables from the data set in a way that allows the plots to convey a lot of information while still being interpreted easily. The plots fulfill 4 or more of the criteria.
Voltage and Power Factor Outlier plot helps the user to quickly identify which substations are out the bounded region.
| dtReadDay | substationName | v.min | v.median | v.min | v.max | pfc.min | pfc.median | pfc.max | desc |
|---|---|---|---|---|---|---|---|---|---|
| 2016-02-28 | BOYNTON VALLEY | 108.60 | 119.40 | 108.60 | 127.80 | 1.00 | 1.02 | 1.07 | Leading |
| 2016-02-29 | BOYNTON VALLEY | 112.10 | 124.05 | 112.10 | 129.00 | NaN | NA | NaN | Leading |
| 2016-03-01 | BOYNTON VALLEY | 107.65 | 118.85 | 107.65 | 127.85 | 1.01 | 1.02 | 1.08 | Leading |
| 2016-03-02 | BOYNTON VALLEY | 111.85 | 122.79 | 111.85 | 128.60 | 1.00 | 1.00 | 1.04 | Leading |
| 2016-03-03 | BOYNTON VALLEY | 108.15 | 119.45 | 108.15 | 127.95 | 1.00 | 1.01 | 1.01 | Leading |
| 2016-03-04 | BOYNTON VALLEY | 111.70 | 122.92 | 111.70 | 127.80 | 1.00 | 1.01 | 1.03 | Leading |
| 2016-03-05 | BOYNTON VALLEY | 110.45 | 119.10 | 110.45 | 127.20 | 1.00 | 1.01 | 1.06 | Leading |
| 2016-03-06 | BOYNTON VALLEY | 113.45 | 123.21 | 113.45 | 129.15 | 1.00 | 1.02 | 1.07 | Leading |
| 2016-03-07 | BOYNTON VALLEY | 106.40 | 119.22 | 106.40 | 126.70 | 1.01 | 1.03 | 1.10 | Leading |
| 2016-03-08 | BOYNTON VALLEY | 113.35 | 123.60 | 113.35 | 128.70 | 1.02 | 1.04 | 1.11 | Leading |
| 2016-03-09 | BOYNTON VALLEY | 105.70 | 119.20 | 105.70 | 128.90 | NA | NA | NA | NA |
| 2016-02-28 | CCJIP | 116.55 | 122.75 | 116.55 | 125.45 | NA | NA | NA | NA |
| 2016-02-29 | CCJIP | 117.35 | 122.49 | 117.35 | 124.65 | NA | NA | NA | NA |
| 2016-03-01 | CCJIP | 117.00 | 122.30 | 117.00 | 124.85 | NA | NA | NA | NA |
| 2016-03-02 | CCJIP | 116.60 | 121.94 | 116.60 | 125.40 | NA | NA | NA | NA |
| 2016-03-03 | CCJIP | 116.25 | 122.70 | 116.25 | 125.55 | NA | NA | NA | NA |
| 2016-03-04 | CCJIP | 116.00 | 121.93 | 116.00 | 125.35 | NA | NA | NA | NA |
| 2016-03-05 | CCJIP | 116.75 | 122.19 | 116.75 | 125.50 | NA | NA | NA | NA |
| 2016-03-06 | CCJIP | 117.55 | 122.42 | 117.55 | 125.30 | NA | NA | NA | NA |
| 2016-03-07 | CCJIP | 115.75 | 122.70 | 115.75 | 125.25 | NA | NA | NA | NA |
| 2016-03-08 | CCJIP | 117.45 | 122.90 | 117.45 | 125.20 | NA | NA | NA | NA |
| 2016-03-09 | CCJIP | 116.15 | 122.70 | 116.15 | 125.10 | NA | NA | NA | NA |
| 2016-02-28 | CHAPEL HILL | 114.95 | 122.88 | 114.95 | 128.20 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-02-29 | CHAPEL HILL | 115.55 | 122.55 | 115.55 | 127.85 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-03-01 | CHAPEL HILL | 113.05 | 122.80 | 113.05 | 127.70 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-03-02 | CHAPEL HILL | 108.95 | 122.20 | 108.95 | 127.70 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-03-03 | CHAPEL HILL | 110.30 | 122.75 | 110.30 | 128.10 | 0.99 | 0.99 | 0.99 | Lagging |
| 2016-03-04 | CHAPEL HILL | 113.35 | 121.96 | 113.35 | 127.70 | 0.99 | 0.99 | 0.99 | Lagging |
| 2016-03-05 | CHAPEL HILL | 114.55 | 122.79 | 114.55 | 128.50 | 0.99 | 0.99 | 0.99 | Lagging |
| 2016-03-06 | CHAPEL HILL | 114.70 | 122.20 | 114.70 | 128.25 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-03-07 | CHAPEL HILL | 114.45 | 123.12 | 114.45 | 128.00 | 0.99 | 0.99 | 0.99 | Lagging |
| 2016-03-08 | CHAPEL HILL | 109.70 | 122.91 | 109.70 | 127.85 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-03-09 | CHAPEL HILL | 110.75 | 122.70 | 110.75 | 127.45 | NA | NA | NA | NA |
| 2016-02-28 | COLUMBIA PRIMARY | 116.00 | 123.42 | 116.00 | 126.60 | NA | NA | NA | NA |
| 2016-02-29 | COLUMBIA PRIMARY | 117.45 | 123.96 | 117.45 | 126.55 | NA | NA | NA | NA |
| 2016-03-01 | COLUMBIA PRIMARY | 116.95 | 122.92 | 116.95 | 126.25 | NA | NA | NA | NA |
| 2016-03-02 | COLUMBIA PRIMARY | 115.65 | 122.25 | 115.65 | 126.10 | NA | NA | NA | NA |
| 2016-03-03 | COLUMBIA PRIMARY | 116.80 | 122.20 | 116.80 | 125.75 | NA | NA | NA | NA |
| 2016-03-04 | COLUMBIA PRIMARY | 115.25 | 122.44 | 115.25 | 126.90 | NA | NA | NA | NA |
| 2016-03-05 | COLUMBIA PRIMARY | 116.20 | 123.10 | 116.20 | 126.65 | NA | NA | NA | NA |
| 2016-03-06 | COLUMBIA PRIMARY | 115.80 | 122.86 | 115.80 | 126.10 | NA | NA | NA | NA |
| 2016-03-07 | COLUMBIA PRIMARY | 117.30 | 124.09 | 117.30 | 127.30 | NA | NA | NA | NA |
| 2016-03-08 | COLUMBIA PRIMARY | 118.25 | 124.39 | 118.25 | 127.50 | NA | NA | NA | NA |
| 2016-03-09 | COLUMBIA PRIMARY | 118.85 | 124.07 | 118.85 | 127.85 | NA | NA | NA | NA |
| 2016-02-28 | CORNERSVILLE | 103.00 | 119.78 | 103.00 | 124.00 | NA | NA | NA | NA |
| 2016-02-29 | CORNERSVILLE | 105.15 | 122.65 | 105.15 | 125.15 | NA | NA | NA | NA |
| 2016-03-01 | CORNERSVILLE | 108.75 | 118.88 | 108.75 | 122.95 | NA | NA | NA | NA |
| 2016-03-02 | CORNERSVILLE | 112.95 | 122.35 | 112.95 | 125.60 | NA | NA | NA | NA |
| 2016-03-03 | CORNERSVILLE | 104.15 | 119.38 | 104.15 | 124.05 | NA | NA | NA | NA |
| 2016-03-04 | CORNERSVILLE | 106.10 | 122.20 | 106.10 | 125.10 | NA | NA | NA | NA |
| 2016-03-05 | CORNERSVILLE | 102.65 | 119.75 | 102.65 | 124.40 | NA | NA | NA | NA |
| 2016-03-06 | CORNERSVILLE | 112.10 | 122.32 | 112.10 | 125.45 | NA | NA | NA | NA |
| 2016-03-07 | CORNERSVILLE | 110.20 | 119.25 | 110.20 | 122.55 | NA | NA | NA | NA |
| 2016-03-08 | CORNERSVILLE | 113.75 | 122.99 | 113.75 | 125.40 | NA | NA | NA | NA |
| 2016-03-09 | CORNERSVILLE | 107.25 | 118.99 | 107.25 | 121.95 | NA | NA | NA | NA |
| 2016-02-28 | COWAN | 119.60 | 126.46 | 119.60 | 129.50 | NA | NA | NA | NA |
| 2016-02-29 | COWAN | 118.85 | 126.82 | 118.85 | 128.80 | NA | NA | NA | NA |
| 2016-03-01 | COWAN | 119.05 | 126.47 | 119.05 | 128.45 | NA | NA | NA | NA |
| 2016-03-02 | COWAN | 119.00 | 125.54 | 119.00 | 128.65 | NA | NA | NA | NA |
| 2016-03-03 | COWAN | 119.25 | 125.79 | 119.25 | 128.85 | NA | NA | NA | NA |
| 2016-03-04 | COWAN | 119.25 | 126.05 | 119.25 | 129.35 | NA | NA | NA | NA |
| 2016-03-05 | COWAN | 118.00 | 126.67 | 118.00 | 129.90 | NA | NA | NA | NA |
| 2016-03-06 | COWAN | 119.85 | 126.59 | 119.85 | 129.95 | NA | NA | NA | NA |
| 2016-03-07 | COWAN | 119.45 | 127.18 | 119.45 | 129.55 | NA | NA | NA | NA |
| 2016-03-08 | COWAN | 120.65 | 127.28 | 120.65 | 129.40 | NA | NA | NA | NA |
| 2016-03-09 | COWAN | 122.60 | 127.41 | 122.60 | 129.40 | NA | NA | NA | NA |
| 2016-02-28 | CULLEOKA | 114.85 | 121.89 | 114.85 | 125.60 | 1.00 | 1.02 | 1.07 | Leading |
| 2016-02-29 | CULLEOKA | 115.85 | 121.78 | 115.85 | 124.40 | 1.01 | 1.03 | 1.07 | Leading |
| 2016-03-01 | CULLEOKA | 114.70 | 121.90 | 114.70 | 125.40 | NaN | NA | NaN | Leading |
| 2016-03-02 | CULLEOKA | 112.15 | 120.85 | 112.15 | 125.40 | 1.00 | 1.00 | 1.04 | Leading |
| 2016-03-03 | CULLEOKA | 114.00 | 122.06 | 114.00 | 125.55 | 1.00 | 1.00 | 1.01 | Leading |
| 2016-03-04 | CULLEOKA | 113.40 | 122.11 | 113.40 | 125.35 | 1.00 | 1.01 | 1.03 | Leading |
| 2016-03-05 | CULLEOKA | 114.20 | 121.89 | 114.20 | 125.55 | 1.00 | 1.01 | 1.07 | Leading |
| 2016-03-06 | CULLEOKA | 114.55 | 121.68 | 114.55 | 125.60 | 1.00 | 1.02 | 1.07 | Leading |
| 2016-03-07 | CULLEOKA | 114.05 | 122.12 | 114.05 | 125.25 | 1.01 | 1.02 | 1.08 | Leading |
| 2016-03-08 | CULLEOKA | 114.95 | 122.78 | 114.95 | 125.45 | 1.02 | 1.03 | 1.07 | Leading |
| 2016-03-09 | CULLEOKA | 115.25 | 123.00 | 115.25 | 125.70 | NA | NA | NA | NA |
| 2016-02-28 | DECHERD | 115.95 | 122.80 | 115.95 | 126.45 | NA | NA | NA | NA |
| 2016-02-29 | DECHERD | 101.35 | 122.92 | 101.35 | 125.95 | NA | NA | NA | NA |
| 2016-03-01 | DECHERD | 115.55 | 122.60 | 115.55 | 126.05 | NA | NA | NA | NA |
| 2016-03-02 | DECHERD | 113.40 | 122.75 | 113.40 | 126.75 | NA | NA | NA | NA |
| 2016-03-03 | DECHERD | 114.25 | 123.40 | 114.25 | 126.75 | NA | NA | NA | NA |
| 2016-03-04 | DECHERD | 100.80 | 122.97 | 100.80 | 126.45 | NA | NA | NA | NA |
| 2016-03-05 | DECHERD | 115.10 | 122.95 | 115.10 | 127.00 | NA | NA | NA | NA |
| 2016-03-06 | DECHERD | 114.90 | 122.40 | 114.90 | 126.65 | NA | NA | NA | NA |
| 2016-03-07 | DECHERD | 102.85 | 122.91 | 102.85 | 125.95 | NA | NA | NA | NA |
| 2016-03-08 | DECHERD | 101.25 | 123.28 | 101.25 | 126.20 | NA | NA | NA | NA |
| 2016-03-09 | DECHERD | 100.60 | 123.70 | 100.60 | 126.20 | NA | NA | NA | NA |
| 2016-02-28 | EAST SHELBYVILLE | 107.20 | 122.40 | 107.20 | 125.60 | NA | NA | NA | NA |
| 2016-02-29 | EAST SHELBYVILLE | 106.90 | 123.00 | 106.90 | 125.40 | NA | NA | NA | NA |
| 2016-03-01 | EAST SHELBYVILLE | 107.65 | 122.05 | 107.65 | 125.30 | NA | NA | NA | NA |
| 2016-03-02 | EAST SHELBYVILLE | 107.25 | 121.50 | 107.25 | 125.55 | NA | NA | NA | NA |
| 2016-03-03 | EAST SHELBYVILLE | 106.80 | 121.93 | 106.80 | 125.65 | NA | NA | NA | NA |
| 2016-03-04 | EAST SHELBYVILLE | 106.35 | 121.67 | 106.35 | 125.45 | NA | NA | NA | NA |
| 2016-03-05 | EAST SHELBYVILLE | 106.55 | 122.32 | 106.55 | 125.60 | NA | NA | NA | NA |
| 2016-03-06 | EAST SHELBYVILLE | 105.85 | 121.86 | 105.85 | 125.50 | NA | NA | NA | NA |
| 2016-03-07 | EAST SHELBYVILLE | 107.40 | 122.32 | 107.40 | 125.55 | NA | NA | NA | NA |
| 2016-03-08 | EAST SHELBYVILLE | 107.65 | 122.83 | 107.65 | 125.85 | NA | NA | NA | NA |
| 2016-03-09 | EAST SHELBYVILLE | 110.00 | 122.96 | 110.00 | 125.25 | NA | NA | NA | NA |
| 2016-02-28 | ESTILL SPRINGS | 112.80 | 122.80 | 112.80 | 126.15 | 0.99 | 1.03 | 1.12 | Leading |
| 2016-02-29 | ESTILL SPRINGS | 115.70 | 123.09 | 115.70 | 125.85 | 0.99 | 1.04 | 1.08 | Leading |
| 2016-03-01 | ESTILL SPRINGS | 110.50 | 122.34 | 110.50 | 125.65 | NaN | NA | NaN | Leading |
| 2016-03-02 | ESTILL SPRINGS | 113.15 | 122.22 | 113.15 | 126.40 | 0.99 | 1.01 | 1.05 | Leading |
| 2016-03-03 | ESTILL SPRINGS | 114.35 | 122.72 | 114.35 | 126.10 | 0.98 | 1.01 | 1.01 | Leading |
| 2016-03-04 | ESTILL SPRINGS | 112.75 | 122.46 | 112.75 | 125.90 | 0.99 | 1.02 | 1.04 | Leading |
| 2016-03-05 | ESTILL SPRINGS | 113.50 | 122.54 | 113.50 | 126.10 | 0.99 | 1.04 | 1.14 | Leading |
| 2016-03-06 | ESTILL SPRINGS | 112.90 | 122.21 | 112.90 | 125.95 | 0.99 | 1.04 | 1.11 | Leading |
| 2016-03-07 | ESTILL SPRINGS | 114.05 | 122.78 | 114.05 | 125.55 | 0.99 | 1.04 | 1.09 | Leading |
| 2016-03-08 | ESTILL SPRINGS | 114.95 | 123.49 | 114.95 | 125.75 | 0.99 | 1.07 | 1.12 | Leading |
| 2016-03-09 | ESTILL SPRINGS | 116.35 | 123.57 | 116.35 | 125.55 | NA | NA | NA | NA |
| 2016-02-28 | FOUNDRY HILL | 112.25 | 122.75 | 112.25 | 126.25 | 1.00 | 1.02 | 1.08 | Leading |
| 2016-02-29 | FOUNDRY HILL | 113.75 | 122.90 | 113.75 | 125.60 | 1.01 | 1.04 | 1.11 | Leading |
| 2016-03-01 | FOUNDRY HILL | 113.15 | 122.75 | 113.15 | 125.65 | NaN | NA | NaN | Leading |
| 2016-03-02 | FOUNDRY HILL | 109.70 | 122.50 | 109.70 | 126.75 | 1.00 | 1.01 | 1.06 | Leading |
| 2016-03-03 | FOUNDRY HILL | 106.55 | 122.35 | 106.55 | 125.95 | NaN | NA | NaN | Leading |
| 2016-03-04 | FOUNDRY HILL | 112.95 | 122.25 | 112.95 | 125.80 | 1.00 | 1.01 | 1.05 | Leading |
| 2016-03-05 | FOUNDRY HILL | 109.60 | 122.72 | 109.60 | 126.45 | 1.00 | 1.02 | 1.08 | Leading |
| 2016-03-06 | FOUNDRY HILL | 110.95 | 122.50 | 110.95 | 126.10 | 1.00 | 1.02 | 1.08 | Leading |
| 2016-03-07 | FOUNDRY HILL | 110.55 | 122.50 | 110.55 | 125.55 | NaN | NA | NaN | Leading |
| 2016-03-08 | FOUNDRY HILL | 110.00 | 123.10 | 110.00 | 125.60 | 1.01 | 1.04 | 1.12 | Leading |
| 2016-03-09 | FOUNDRY HILL | 113.45 | 122.97 | 113.45 | 125.45 | NA | NA | NA | NA |
| 2016-02-28 | HILLSBORO | 114.05 | 122.33 | 114.05 | 126.20 | 0.99 | 1.02 | 1.06 | Leading |
| 2016-02-29 | HILLSBORO | 112.10 | 122.40 | 112.10 | 125.90 | 0.99 | 1.03 | 1.08 | Leading |
| 2016-03-01 | HILLSBORO | 111.40 | 121.78 | 111.40 | 125.65 | NaN | NA | NaN | Leading |
| 2016-03-02 | HILLSBORO | 109.90 | 121.75 | 109.90 | 126.05 | 0.99 | 1.00 | 1.03 | Leading |
| 2016-03-03 | HILLSBORO | 112.00 | 122.10 | 112.00 | 126.00 | 0.98 | 1.00 | 1.01 | Leading |
| 2016-03-04 | HILLSBORO | 110.10 | 122.00 | 110.10 | 126.05 | 0.99 | 1.01 | 1.03 | Leading |
| 2016-03-05 | HILLSBORO | 113.30 | 122.07 | 113.30 | 126.20 | 0.99 | 1.02 | 1.05 | Leading |
| 2016-03-06 | HILLSBORO | 108.65 | 121.80 | 108.65 | 126.25 | 0.99 | 1.02 | 1.06 | Leading |
| 2016-03-07 | HILLSBORO | 113.50 | 122.50 | 113.50 | 125.40 | 0.99 | 1.03 | 1.07 | Leading |
| 2016-03-08 | HILLSBORO | 113.85 | 122.20 | 113.85 | 125.45 | 0.98 | 1.03 | 1.06 | Leading |
| 2016-03-09 | HILLSBORO | 112.10 | 122.43 | 112.10 | 125.40 | NA | NA | NA | NA |
| 2016-02-28 | KS PHILLIPS | 117.10 | 123.40 | 117.10 | 126.10 | 1.00 | 1.00 | 1.00 | Lagging |
| 2016-02-29 | KS PHILLIPS | 117.80 | 123.47 | 117.80 | 125.75 | 0.99 | 1.00 | 1.00 | Lagging |
| 2016-03-01 | KS PHILLIPS | 117.35 | 123.54 | 117.35 | 126.10 | NaN | NA | NaN | Lagging |
| 2016-03-02 | KS PHILLIPS | 115.25 | 123.45 | 115.25 | 126.55 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-03-03 | KS PHILLIPS | 116.45 | 123.78 | 116.45 | 126.35 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-03-04 | KS PHILLIPS | 116.15 | 122.97 | 116.15 | 126.25 | 0.99 | 1.00 | 1.00 | Lagging |
| 2016-03-05 | KS PHILLIPS | 116.15 | 123.19 | 116.15 | 126.05 | 0.99 | 1.00 | 1.00 | Lagging |
| 2016-03-06 | KS PHILLIPS | 117.45 | 123.22 | 117.45 | 125.85 | 0.99 | 1.00 | 1.00 | Lagging |
| 2016-03-07 | KS PHILLIPS | 116.55 | 123.60 | 116.55 | 125.95 | 0.99 | 1.00 | 1.00 | Lagging |
| 2016-03-08 | KS PHILLIPS | 117.05 | 123.72 | 117.05 | 125.80 | 0.99 | 0.99 | 1.00 | Lagging |
| 2016-03-09 | KS PHILLIPS | 117.35 | 123.32 | 117.35 | 125.30 | NA | NA | NA | NA |
| 2016-02-28 | LYNCHBURG | 116.10 | 123.22 | 116.10 | 127.15 | 0.98 | 1.00 | 1.01 | Lagging |
| 2016-02-29 | LYNCHBURG | 117.50 | 123.56 | 117.50 | 127.05 | 0.98 | 1.00 | 1.00 | Lagging |
| 2016-03-01 | LYNCHBURG | 116.90 | 123.36 | 116.90 | 126.80 | NaN | NA | NaN | Lagging |
| 2016-03-02 | LYNCHBURG | 116.60 | 123.34 | 116.60 | 127.05 | 0.98 | 1.00 | 1.00 | Lagging |
| 2016-03-03 | LYNCHBURG | 115.50 | 123.32 | 115.50 | 126.75 | 0.97 | 1.00 | 1.00 | Lagging |
| 2016-03-04 | LYNCHBURG | 116.85 | 123.38 | 116.85 | 127.10 | 0.98 | 1.00 | 1.00 | Lagging |
| 2016-03-05 | LYNCHBURG | 116.40 | 123.45 | 116.40 | 127.35 | 0.98 | 1.00 | 1.00 | Lagging |
| 2016-03-06 | LYNCHBURG | 115.25 | 123.03 | 115.25 | 127.10 | 0.98 | 1.00 | 1.01 | Lagging |
| 2016-03-07 | LYNCHBURG | 117.30 | 123.43 | 117.30 | 127.05 | 0.98 | 1.00 | 1.00 | Lagging |
| 2016-03-08 | LYNCHBURG | 117.10 | 123.93 | 117.10 | 127.20 | 0.98 | 1.00 | 1.00 | Lagging |
| 2016-03-09 | LYNCHBURG | 118.80 | 124.20 | 118.80 | 127.20 | NA | NA | NA | NA |
| 2016-02-28 | MANCHESTER 161 | 113.80 | 122.82 | 113.80 | 126.90 | NA | NA | NA | NA |
| 2016-02-29 | MANCHESTER 161 | 113.55 | 122.72 | 113.55 | 125.50 | NA | NA | NA | NA |
| 2016-03-01 | MANCHESTER 161 | 112.20 | 122.22 | 112.20 | 125.70 | NA | NA | NA | NA |
| 2016-03-02 | MANCHESTER 161 | 108.45 | 121.12 | 108.45 | 124.65 | NA | NA | NA | NA |
| 2016-03-03 | MANCHESTER 161 | 112.90 | 121.31 | 112.90 | 124.35 | NA | NA | NA | NA |
| 2016-03-04 | MANCHESTER 161 | 111.50 | 121.55 | 111.50 | 125.25 | NA | NA | NA | NA |
| 2016-03-05 | MANCHESTER 161 | 110.20 | 122.75 | 110.20 | 126.65 | NA | NA | NA | NA |
| 2016-03-06 | MANCHESTER 161 | 112.80 | 122.30 | 112.80 | 125.95 | NA | NA | NA | NA |
| 2016-03-07 | MANCHESTER 161 | 112.80 | 122.80 | 112.80 | 125.40 | NA | NA | NA | NA |
| 2016-03-08 | MANCHESTER 161 | 114.70 | 122.95 | 114.70 | 125.40 | NA | NA | NA | NA |
| 2016-03-09 | MANCHESTER 161 | 117.10 | 122.70 | 117.10 | 125.85 | NA | NA | NA | NA |
| 2016-02-28 | MT PLEASANT | 116.60 | 122.49 | 116.60 | 128.20 | NA | NA | NA | NA |
| 2016-02-29 | MT PLEASANT | 116.10 | 123.17 | 116.10 | 127.50 | NA | NA | NA | NA |
| 2016-03-01 | MT PLEASANT | 115.10 | 122.88 | 115.10 | 126.90 | NA | NA | NA | NA |
| 2016-03-02 | MT PLEASANT | 116.35 | 121.65 | 116.35 | 127.05 | NA | NA | NA | NA |
| 2016-03-03 | MT PLEASANT | 115.70 | 121.76 | 115.70 | 126.30 | NA | NA | NA | NA |
| 2016-03-04 | MT PLEASANT | 115.10 | 121.90 | 115.10 | 127.65 | NA | NA | NA | NA |
| 2016-03-05 | MT PLEASANT | 113.45 | 122.25 | 113.45 | 127.95 | NA | NA | NA | NA |
| 2016-03-06 | MT PLEASANT | 116.95 | 122.55 | 116.95 | 127.80 | NA | NA | NA | NA |
| 2016-03-07 | MT PLEASANT | 117.75 | 123.01 | 117.75 | 127.90 | NA | NA | NA | NA |
| 2016-03-08 | MT PLEASANT | 117.25 | 123.78 | 117.25 | 127.75 | NA | NA | NA | NA |
| 2016-03-09 | MT PLEASANT | 117.85 | 123.96 | 117.85 | 128.40 | NA | NA | NA | NA |
| 2016-02-28 | RALLY HILL | 115.45 | 122.29 | 115.45 | 125.55 | 1.00 | 1.03 | 1.08 | Leading |
| 2016-02-29 | RALLY HILL | 116.90 | 122.71 | 116.90 | 125.70 | 1.00 | 1.02 | 1.05 | Leading |
| 2016-03-01 | RALLY HILL | 114.75 | 122.12 | 114.75 | 125.45 | 1.00 | 1.03 | 1.05 | Leading |
| 2016-03-02 | RALLY HILL | 112.40 | 121.31 | 112.40 | 125.25 | 0.99 | 1.00 | 1.03 | Leading |
| 2016-03-03 | RALLY HILL | 113.95 | 121.44 | 113.95 | 124.85 | 0.99 | 1.01 | 1.01 | Leading |
| 2016-03-04 | RALLY HILL | 115.15 | 121.65 | 115.15 | 124.80 | 1.00 | 1.01 | 1.03 | Leading |
| 2016-03-05 | RALLY HILL | 110.20 | 121.64 | 110.20 | 125.15 | 1.00 | 1.01 | 1.06 | Leading |
| 2016-03-06 | RALLY HILL | 110.10 | 121.93 | 110.10 | 124.85 | 1.00 | 1.02 | 1.08 | Leading |
| 2016-03-07 | RALLY HILL | 112.30 | 122.03 | 112.30 | 124.85 | 1.00 | 1.01 | 1.07 | Leading |
| 2016-03-08 | RALLY HILL | 111.45 | 122.35 | 111.45 | 125.50 | 1.00 | 1.01 | 1.06 | Leading |
| 2016-03-09 | RALLY HILL | 110.15 | 122.35 | 110.15 | 124.80 | NA | NA | NA | NA |
| 2016-02-28 | RED HILL | 116.60 | 123.00 | 116.60 | 125.50 | 0.94 | 0.99 | 1.00 | Lagging |
| 2016-02-29 | RED HILL | 117.70 | 122.84 | 117.70 | 124.95 | 0.93 | 0.97 | 0.98 | Lagging |
| 2016-03-01 | RED HILL | 116.55 | 122.99 | 116.55 | 124.75 | NaN | NA | NaN | Lagging |
| 2016-03-02 | RED HILL | 116.55 | 122.95 | 116.55 | 124.70 | 0.95 | 0.98 | 0.98 | Lagging |
| 2016-03-03 | RED HILL | 117.60 | 123.04 | 117.60 | 124.90 | 0.95 | 0.98 | 0.99 | Lagging |
| 2016-03-04 | RED HILL | 117.75 | 122.99 | 117.75 | 124.90 | 0.94 | 0.98 | 0.99 | Lagging |
| 2016-03-05 | RED HILL | 117.45 | 122.71 | 117.45 | 125.35 | 0.96 | 0.99 | 1.00 | Lagging |
| 2016-03-06 | RED HILL | 116.70 | 123.00 | 116.70 | 124.95 | 0.94 | 0.98 | 1.01 | Lagging |
| 2016-03-07 | RED HILL | 118.15 | 122.60 | 118.15 | 124.95 | 0.94 | 0.97 | 0.98 | Lagging |
| 2016-03-08 | RED HILL | 118.10 | 122.75 | 118.10 | 124.75 | 0.94 | 0.97 | 0.98 | Lagging |
| 2016-03-09 | RED HILL | 117.40 | 122.60 | 117.40 | 124.70 | NA | NA | NA | NA |
| 2016-02-28 | SALEM | 115.40 | 123.26 | 115.40 | 126.15 | NA | NA | NA | NA |
| 2016-02-29 | SALEM | 115.25 | 122.95 | 115.25 | 125.45 | NA | NA | NA | NA |
| 2016-03-01 | SALEM | 116.25 | 122.69 | 116.25 | 125.90 | NA | NA | NA | NA |
| 2016-03-02 | SALEM | 112.70 | 122.58 | 112.70 | 126.10 | NA | NA | NA | NA |
| 2016-03-03 | SALEM | 115.05 | 122.51 | 115.05 | 126.15 | NA | NA | NA | NA |
| 2016-03-04 | SALEM | 115.00 | 122.45 | 115.00 | 126.40 | NA | NA | NA | NA |
| 2016-03-05 | SALEM | 114.55 | 122.66 | 114.55 | 126.45 | NA | NA | NA | NA |
| 2016-03-06 | SALEM | 114.25 | 122.46 | 114.25 | 125.85 | NA | NA | NA | NA |
| 2016-03-07 | SALEM | 114.35 | 123.14 | 114.35 | 126.05 | NA | NA | NA | NA |
| 2016-03-08 | SALEM | 113.75 | 123.19 | 113.75 | 126.15 | NA | NA | NA | NA |
| 2016-03-09 | SALEM | 115.30 | 122.72 | 115.30 | 126.10 | NA | NA | NA | NA |
| 2016-02-28 | SEWANEE | 113.10 | 120.86 | 113.10 | 123.15 | NA | NA | NA | NA |
| 2016-02-29 | SEWANEE | 114.30 | 121.07 | 114.30 | 123.50 | NA | NA | NA | NA |
| 2016-03-01 | SEWANEE | 114.70 | 120.82 | 114.70 | 123.30 | NA | NA | NA | NA |
| 2016-03-02 | SEWANEE | 113.85 | 120.39 | 113.85 | 123.15 | NA | NA | NA | NA |
| 2016-03-03 | SEWANEE | 113.75 | 119.70 | 113.75 | 122.75 | NA | NA | NA | NA |
| 2016-03-04 | SEWANEE | 114.20 | 120.16 | 114.20 | 122.95 | NA | NA | NA | NA |
| 2016-03-05 | SEWANEE | 113.70 | 120.85 | 113.70 | 123.25 | NA | NA | NA | NA |
| 2016-03-06 | SEWANEE | 113.90 | 120.66 | 113.90 | 123.35 | NA | NA | NA | NA |
| 2016-03-07 | SEWANEE | 115.25 | 120.80 | 115.25 | 123.40 | NA | NA | NA | NA |
| 2016-03-08 | SEWANEE | 114.95 | 121.14 | 114.95 | 123.15 | NA | NA | NA | NA |
| 2016-03-09 | SEWANEE | 115.55 | 121.32 | 115.55 | 123.30 | NA | NA | NA | NA |
| 2016-02-28 | SPRING HILL | 116.75 | 123.88 | 116.75 | 125.75 | 1.01 | 1.02 | 1.05 | Leading |
| 2016-02-29 | SPRING HILL | 116.55 | 123.60 | 116.55 | 125.70 | 1.02 | 1.05 | 1.06 | Leading |
| 2016-03-01 | SPRING HILL | 116.30 | 123.32 | 116.30 | 125.30 | NaN | NA | NaN | Leading |
| 2016-03-02 | SPRING HILL | 115.85 | 123.60 | 115.85 | 126.10 | 1.00 | 1.01 | 1.05 | Leading |
| 2016-03-03 | SPRING HILL | 115.55 | 123.74 | 115.55 | 125.95 | 1.00 | 1.01 | 1.01 | Leading |
| 2016-03-04 | SPRING HILL | 116.15 | 123.50 | 116.15 | 125.75 | 1.01 | 1.01 | 1.03 | Leading |
| 2016-03-05 | SPRING HILL | 116.85 | 123.55 | 116.85 | 126.40 | 1.00 | 1.02 | 1.05 | Leading |
| 2016-03-06 | SPRING HILL | 117.30 | 123.58 | 117.30 | 125.70 | 1.00 | 1.02 | 1.04 | Leading |
| 2016-03-07 | SPRING HILL | 116.80 | 123.88 | 116.80 | 125.45 | 1.02 | 1.03 | 1.06 | Leading |
| 2016-03-08 | SPRING HILL | 113.25 | 123.93 | 113.25 | 125.35 | 1.02 | 1.04 | 1.06 | Leading |
| 2016-03-09 | SPRING HILL | 116.30 | 123.30 | 116.30 | 125.20 | NA | NA | NA | NA |
| 2016-02-28 | UNIONVILLE | 111.55 | 119.60 | 111.55 | 123.10 | 1.00 | 1.02 | 1.04 | Leading |
| 2016-02-29 | UNIONVILLE | 116.90 | 122.72 | 116.90 | 125.45 | 1.02 | 1.04 | 1.05 | Leading |
| 2016-03-01 | UNIONVILLE | 111.65 | 119.28 | 111.65 | 123.65 | NaN | NA | NaN | Leading |
| 2016-03-02 | UNIONVILLE | 111.55 | 121.80 | 111.55 | 125.90 | 1.00 | 1.00 | 1.03 | Leading |
| 2016-03-03 | UNIONVILLE | 110.90 | 119.72 | 110.90 | 123.90 | 1.00 | 1.00 | 1.01 | Leading |
| 2016-03-04 | UNIONVILLE | 112.90 | 122.43 | 112.90 | 125.15 | 1.00 | 1.01 | 1.02 | Leading |
| 2016-03-05 | UNIONVILLE | 110.85 | 119.59 | 110.85 | 123.90 | 1.00 | 1.01 | 1.04 | Leading |
| 2016-03-06 | UNIONVILLE | 113.05 | 122.28 | 113.05 | 125.50 | 1.00 | 1.02 | 1.04 | Leading |
| 2016-03-07 | UNIONVILLE | 110.50 | 119.25 | 110.50 | 122.15 | 1.01 | 1.03 | 1.07 | Leading |
| 2016-03-08 | UNIONVILLE | 114.95 | 122.97 | 114.95 | 125.35 | 1.02 | 1.04 | 1.06 | Leading |
| 2016-03-09 | UNIONVILLE | 105.05 | 119.50 | 105.05 | 121.60 | NA | NA | NA | NA |
| 2016-02-28 | WARTRACE | 112.00 | 119.62 | 112.00 | 124.00 | 1.00 | 1.01 | 1.06 | Leading |
| 2016-02-29 | WARTRACE | 115.55 | 122.88 | 115.55 | 125.55 | 1.00 | 1.01 | 1.02 | Leading |
| 2016-03-01 | WARTRACE | 109.10 | 118.95 | 109.10 | 123.95 | NaN | NA | NaN | Leading |
| 2016-03-02 | WARTRACE | 112.15 | 121.81 | 112.15 | 126.15 | 1.00 | 1.00 | 1.01 | Leading |
| 2016-03-03 | WARTRACE | 111.35 | 119.69 | 111.35 | 123.80 | 1.00 | 1.00 | 1.00 | Leading |
| 2016-03-04 | WARTRACE | 115.00 | 122.35 | 115.00 | 125.75 | 1.00 | 1.00 | 1.01 | Leading |
| 2016-03-05 | WARTRACE | 111.10 | 119.62 | 111.10 | 124.85 | 1.00 | 1.01 | 1.03 | Leading |
| 2016-03-06 | WARTRACE | 113.45 | 122.38 | 113.45 | 125.60 | 1.00 | 1.01 | 1.03 | Leading |
| 2016-03-07 | WARTRACE | 113.20 | 119.03 | 113.20 | 123.10 | 1.00 | 1.03 | 1.04 | Leading |
| 2016-03-08 | WARTRACE | 114.75 | 122.95 | 114.75 | 125.35 | 1.01 | 1.01 | 1.03 | Leading |
| 2016-03-09 | WARTRACE | 111.75 | 119.45 | 111.75 | 124.90 | NA | NA | NA | NA |
| 2016-02-28 | WILLIAMSPORT | 113.65 | 123.86 | 113.65 | 127.85 | NA | NA | NA | NA |
| 2016-02-29 | WILLIAMSPORT | 115.95 | 124.14 | 115.95 | 127.65 | NA | NA | NA | NA |
| 2016-03-01 | WILLIAMSPORT | 112.80 | 122.61 | 112.80 | 127.55 | NA | NA | NA | NA |
| 2016-03-02 | WILLIAMSPORT | 112.85 | 121.78 | 112.85 | 127.30 | NA | NA | NA | NA |
| 2016-03-03 | WILLIAMSPORT | 114.55 | 122.76 | 114.55 | 126.50 | NA | NA | NA | NA |
| 2016-03-04 | WILLIAMSPORT | 114.60 | 122.78 | 114.60 | 128.20 | NA | NA | NA | NA |
| 2016-03-05 | WILLIAMSPORT | 114.25 | 123.46 | 114.25 | 127.45 | NA | NA | NA | NA |
| 2016-03-06 | WILLIAMSPORT | 115.65 | 122.81 | 115.65 | 126.85 | NA | NA | NA | NA |
| 2016-03-07 | WILLIAMSPORT | 115.80 | 124.88 | 115.80 | 128.15 | NA | NA | NA | NA |
| 2016-03-08 | WILLIAMSPORT | 116.00 | 124.80 | 116.00 | 127.85 | NA | NA | NA | NA |
| 2016-03-09 | WILLIAMSPORT | 116.55 | 124.89 | 116.55 | 126.90 | NA | NA | NA | NA |
This chart represents the voltage and power factor for all data points collected for 2016-03-02. The chart allows one to see how much of the data falls out of the voltage and power factor defined region (0.98 (left) to 0.99 (right), and 114v to 126v).
We can see we have more points with Lagging power factors outside the required range for efficient power factor values.
NEED 4 or more…
1 Draw comparisons. * YES - Comparisons between substations 2 Identify trends.
* NO - Trends - Nope, need multiple days… or day without VVar day with VVar 3 Engage a wide audience. * Yes, easy to review… 4 Explain a complicated finding.
* YES - which substations behaved and what where their min and max for pf and voltages 5 Clarify a gap between perception and reality.
* MAYBE - If a person looks at the current momentary… status * the daily status might be different 6 Enable the reader to digest large amounts of information. * YES - Power Factor, Voltage, Ranges, inside the comfort zone,
Heatmap of Voltage to help user to see how all substations performed during each 15 minute period during the day. One chart helps to see who is in the red (low) or in the purple (high) voltage ranges.
1 Draw comparisons.
2 Identify trends.
3 Engage a wide audience.
4 Explain a complicated finding.
5 Clarify a gap between perception and reality.
6 Enable the reader to digest large amounts of information.
Description goes here please… Note, I think I have a better plot to put here, this one kinda goes along with Plot_One, it is just another form of the plot.
Total
The section explains any important decisions in the analysis and how those decisions affected the analysis.
Voltage seems to be normally like distributed by the hour through out the day and by substation for the entire by hour. This seems good since large flucuations in voltage are bad for consumers and commercial businesses.
Power Factor can flucuation from lagging to leading in on substation in a single day. This requires more effort to control the voltage , watts, and vars across the power lines. Can I show that when voltage is tightly controled we have less variability in the power factor??????
TODO: Find a sub with the best range in voltage and look at its power factor. Then compare it’s MW and MVARS to all other substations.
The section reflects on how the analysis was conducted and reports on the struggles and successes throughout the analysis. The section provides at least one idea or question for future work.
The section provides a rich and well-written reflection of * struggles * Date Formatting * Exploring for relationships
The section poses ideas or questions for future work. * multiple days would be benficial * months to months * 12 month analysis of trends * seasonal trends * collect more power factor data down to 15min intervals
We monitor voltage since a utility can change the voltage across the power lines. Keeping the system balanced at low voltages between 114 and 126 helps to improve the efficeny of the power transmission since lower voltages mean less resistance.
I = Current
R = Resistance
V = Voltage
\(V = I*R\)
\(R = \frac{V}{I}\)
\(I = \frac{V}{R}\)
P(kW) = PF × I(A) × V(V) / 1000
Power - kW, kiloWatts
Power Factor
I - Amps
Voltage - Volts
3 Phase
P(kW) = √3 × PF × I(A) × VL-L(V) / 1000
P, Real Power - kW -> PMWD3D (MW) Real power is kilowatts, in the initial dataset this is represented as PMWD3D.
S, Apparent Power
We will be solving for apparent power.
Q, Reactive Power Reactive power is kVAR, in the initial dataset PMQD3D is Delivered and
PMQR3D is Received. The total kVars are (PMQD3D-PMQR3D).
\(S^2 = P^2 + Q^2\)
\(S=\sqrt{P^2 + Q^2}\)
The power factor is defined as the ratio of real power to apparent power.
\[Power Factor=\frac{P}{\sqrt{P^2 + Q^2}}\]
\[Power Factor =\frac{kW}{\sqrt{kW^2 + (kVAR Delieverd - kVAR Received)^2}}\]
https://en.wikipedia.org/wiki/Power_factor
https://en.wikipedia.org/wiki/Ohm%27s_law
https://en.wikipedia.org/wiki/Volt-ampere_reactive
http://www.statpower.net/Content/310/R%20Stuff/SampleMarkdown.html
http://rmarkdown.rstudio.com/authoring_basics.html
http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm
http://vita.had.co.nz/papers/tidy-data.pdf http://adv-r.had.co.nz/Style.html
https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf http://www.r-statistics.com/2012/03/do-more-with-dates-and-times-in-r-with-lubridate-1-1-0/ http://www.statmethods.net/management/aggregate.html http://yihui.name/knitr/options/ http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html http://kbroman.org/knitr_knutshell/pages/Rmarkdown.html http://data.princeton.edu/R/linearModels.html https://statistics.laerd.com/statistical-guides/pearson-correlation-coefficient-statistical-guide.php